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Update app.py
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app.py
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@@ -11,7 +11,8 @@ import uuid
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from pathlib import Path
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from huggingface_hub import CommitScheduler, delete_file, hf_hub_download
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from gradio_client import Client
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####################################
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# Constants
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####################################
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@@ -26,6 +27,7 @@ AVAILABLE_MODELS = {
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SPACE_ID = os.getenv('HF_ID')
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MAX_SAMPLE_TXT_LENGTH = 150
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DB_DATASET_ID = os.getenv('DATASET_ID')
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DB_NAME = "database.db"
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@@ -444,6 +446,10 @@ def synthandreturn(text):
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text = text.strip()
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if len(text) > MAX_SAMPLE_TXT_LENGTH:
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raise gr.Error(f'You exceeded the limit of {MAX_SAMPLE_TXT_LENGTH} characters')
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if not text:
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raise gr.Error(f'You did not enter any text')
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# Get two random models
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from pathlib import Path
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from huggingface_hub import CommitScheduler, delete_file, hf_hub_download
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from gradio_client import Client
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from detoxify import Detoxify
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toxicity = Detoxify('original')
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####################################
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# Constants
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####################################
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SPACE_ID = os.getenv('HF_ID')
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MAX_SAMPLE_TXT_LENGTH = 150
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MIN_SAMPLE_TXT_LENGTH = 10
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DB_DATASET_ID = os.getenv('DATASET_ID')
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DB_NAME = "database.db"
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text = text.strip()
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if len(text) > MAX_SAMPLE_TXT_LENGTH:
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raise gr.Error(f'You exceeded the limit of {MAX_SAMPLE_TXT_LENGTH} characters')
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if len(text) < MIN_SAMPLE_TXT_LENGTH:
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raise gr.Error(f'Not enough text')
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if (toxicity.predict(text)['toxicity'] > 0.5):
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raise gr.Error('Your text failed the toxicity test!')
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if not text:
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raise gr.Error(f'You did not enter any text')
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# Get two random models
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